Since the emergence of communication networks, various devices were invented and various existing devices, apparatuses, machines, objects and so on were merged into such communication networks for the ease of not only communications but also attractive applications for human-beings. Internet of Things (IoT) is an example of such tremendous expansion of networks. The term of IoT was proposed in 1990s and refers to uniquely identifiable objects and their virtual representations in an Internet-like structure. From then on, different definitions for IoT have appeared and the term of IoT is evolving as the technology and implementation of the ideas move forward.
Briefly speaking, IoT is going to create a world where physical objects are seamlessly integrated into information networks thus advanced and intelligent services can be provided for kinds of users. The interconnected “things”, such as sensors or mobile devices or the like, sense, monitor and collect all kinds of data about human social life. The collected data can be processed for subsequent usages. For example, the data can be further split, aggregated, analyzed, computed or processed in any desired ways, to extract or obtain information to enable intelligent and ubiquitous services. In these days, IoT has evolved as an attractive next generation networking paradigm and service infrastructure. Various applications and services of IoT have emerged in various fields such as surveillance, health care, security, transportation, food safety, distant object monitor and control, etc. The future of IoT is promising.
Further, the wide expansion of IoT facilitates the evolving of another technology which is called cloud computing. Cloud computing offers a new way of service provision by re-arranging various resources for example such as storage, data computing and applications and by providing the ones as required by users, thus provides a large resource pool by linking network resources together. Upon cooperating with IoT, cloud computing can provide computing services to take over data processing loaded at personal terminals, hosts or even some service providers, wherein said data processing is generally big, heavy or complex for the parties maintaining the data, and thus are desired for relatively professional or dedicated processions by certain parties or apparatuses.
One practical scenario is that data monitored or sensed in the network, such as IoT, (from the “things” like mobile devices or the like) can be aggregated or collected, and sent to the cloud. A cloud service provider (CSP) of the cloud in turn processes received data and provides data computing results to requesting parties, such as an IoT service provider that may be offered by another CSP. In this case, the overall capability of data processions is enhanced and QoS is improved, which fueled even quicker expansions of network-related services. However, integrating difference parties together and arranging them to cooperate with each other will certainly raise an issue which cannot be neglected, i.e., the security.
The concept of Privacy Preserving Data Mining (PPDM) is proposed for supporting to provide various IoT services securely and intelligently in a pervasive and personalized way. In practice, this is still a challenge especially when considering computation complexity and communication cost.
Secure multi-party computation (SMC) is introduced into the scenario for secure computation among participants (who are not trusted with each other), particularly with the preference of privacy preserving computational geometry. In this case multiple parties participate in the computation with their own secret inputs, and wish to cooperatively compute a function. It is desired that each party can receive its own correct output and thus knows its own output only after finishing the cooperated computation, then privacy is preserved.
In order to deal with security-related considerations some of which are mentioned in the above, several schemes are proposed. For example, a new architecture was proposed to enable SMC by hiding the identity of the parties (by for example taking part in the process of Business Process Outsourcing). A class of functions was employed to enable a party to split its huge data before submitting it to CSP for computation. Upon such processions, the process data is almost intractable for other parties to know its actual source, thereby secure and privacy-preserved data collections are possible.
A privacy-preserving sequential pattern mining solution was designed based on secure multi-party sum protocol and secure multi-party multi-data ranking protocol for privacy-preserving consumptive action analysis of multi-marketplace, privacy-preserving disease diagnose of multi-hospital and so on.
Further, schemes for securely extracting knowledge from two or more parties' private data were also proposed. Upon study of privacy-preserving Add and Multiply Exchanging Technology, three different approaches to privacy-preserving Add to Multiply Protocol were designed, and further extension to privacy-preserving Adding to Scalar Product Protocol has been proposed. A private-preserving shared dot product protocol that is a main building block of various data mining algorithms with privacy concerns has been studied and fundamental security guarantee for many PPDM algorithms becomes possible. Wherein, a privacy-preserving two-party shared dot product protocol based on some basic cryptographic techniques, which is provably secure in a malicious model in the semi-honest model, is constructed. A HDPPDK-Means (Horizontal Distribution of the Privacy Protection DK-Means) algorithm based on Horizontal partitioned database and DK-means idea is proposed to realize distributed clustering, thus a secure multi-party computation protocol is applied to achieve the Privacy Preserving objective. Other examples such as statistical test, association rule mining, a generic formulation of secure gradient descent methods with privacy preservation, various encryption such as homomorphic encryption, are also utilized in various fields.
However, current researches in the field mainly focus on auditing cloud data storage and data integrity with regard to data operations, such as insertion, deletion, and addition, but none of them care about the security of the party processing cloud data, such as the correctness of data processing like calculation and computation (especially the correctness of encrypted collected data), the facticity of the data or the like. While in practice, the parties responsible for such heavily and important data storage or maintaining, computing, processions and so on, such as the above mentioned CSP, are possibly not fully trusted, for both the data source (for example the above IoT data provider) and/or the requesting party like the above another IoT service provider, or a user terminal, etc.
For example, CSP may acts as an untrustworthy party by malicious miming the raw data obtain from an IoT data provider, and provides such processed data to a third party, i.e., a party requested this CSP for the data collected from that IoT data provider. In this case, upon further services offered by the requesting party based on such wrong IoT data computing results, the service quality thereof can be degraded intentionally. From this we can see that how to ensure the facticity of data sources, the correctness of IoT data processing, computing, as well as mining, becomes a practically crucial issue that greatly impacts the overall user experience.
Unfortunately, the solutions the inventors know don't care about the potential risks at the CSP side.
The above background illustrates the environment of the invention and considerations of inventors. This part is mainly introduced under the related requirements by law and for ease understanding the original purpose of this invention. However, this does not mean that the information given in this part is admitted as prior art. In other words, it is not necessarily that the content above is part of prior art, it is quite possible that some or most of it is just known by the inventors rather than all those skilled in the art.